Volume 2 Supplement 1

Metabolism, Diet and Disease 2014: Cancer and metabolism

Open Access

LC-MS and GC-MS based metabolomics platform for cancer research

  • Wenyun Lu1,
  • Sisi Zhang1, 2,
  • Xin Teng1,
  • Eugene Melamud1,
  • Mitchell A Lazar3,
  • Eileen White2 and
  • Joshua D Rabinowitz1, 2
Cancer & Metabolism20142(Suppl 1):P41

https://doi.org/10.1186/2049-3002-2-S1-P41

Published: 28 May 2014

We present here a LC-MS and GC-MS based analytical platform for the comprehensive analysis of cellular metabolites, including water-soluble metabolites, and water-insoluble fatty acids, and phospholipids. The entire workflow consists of metabolic extraction, LC-MS and GC-MS runs, data analysis and interpretation. Standard operation procedures have been developed for the metabolite extraction from cell culture, tissue and serum/plasma, which involve liquid extraction using appropriate extraction solvents. Metabolites were analyzed using multiple analytical methods on multiple dedicated instruments. Cationic water-soluble metabolites were analyzed on a triple quadrupole instrument using hydrophilic interaction chromatography [1]. Anionic water-soluble metabolites were analyzed using high resolution ‘Exactive’ Orbitrap mass spectrometer coupled to reversed phase ion pairing chromatography [2]. Fatty acids and phospholipids were analyzed using Agilent Q-TOF instrument coupled to reversed phase chromatography [3]. Data analyses were performed using MAVEN program which converts the raw data into a validated table of metabolite-specific signals [4]. Examples will be provided to demonstrate our capability to analyze a broad range of metabolites from real biological samples, as well as to probe the metabolic fluxes using stable isotope tracers.

Declarations

Acknowledgements

This research was supported by NIH grant P50 GM071508 for Center of Quantitative Biology at Princeton University. Additional support comes from DRC at the University of Pennsylvania through grant NIH DK P30-19525, and Rutgers Cancer Institute of New Jersey through grant P30-CA072720.

Authors’ Affiliations

(1)
Lewis-Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University
(2)
Rutgers Cancer Institute of New Jersey
(3)
Diabetes Research Center, University of Pennsylvania

References

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Copyright

© Lu et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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